The subject application is directly broadly to image enhancement, and is particularly applicable to captured images of backlit specimens. However, it will be appreciated that the concepts disclosed herein are particularly applicable to any image enhancement wherein two or more portions of captured image have distinct lighting, brightness, or contrast characteristics.
Electronically encoded images are ubiquitous. Today, such images may be captured directly from a device, such as a digital still camera or digital video recorder, scanned in from other media, such as photographs, captured from streaming media, such as a live television feed, or consist of one or more previously obtained images retrieved from storage, such as from numerically encoded image archives. Many such images were either captured under less-than-ideal conditions, or with equipment that rendered a resulting image less than optimal due to variations in lighting or other properties on various aspects of a captured image. One example is images that are taken in a backlit setting. Such a situation may result when a bright sky, direct sunlight, or any other relatively intense background illumination source is situated behind an object of interest, such as a building, person or landscape feature. The background illumination in such a situation is sufficiently intense that detail or resolution of the foreground image or object, the backlit image portion, or both is compromised. Earlier approaches to address such concerns have been made algorithmically, electrically, via signal processing or mechanically (such as through filtration, f-stop, aperture size, and the like during image capture). However, earlier systems focused on capture or processing of an image as a whole, such that attempts to address concerns for one portion of an image would adversely impact other aspects of the image.
Captured or stored images are typically stored in an encoded format, such as digitally, which encoding is often done in connection with component values of a primary color space. Such color components are suitably additive in nature, such as red-green-blue (RGB), or subtractive, such as cyan, yellow, magenta (CYM), the latter of which is frequently coupled with a black color (K), referred to as CYMK or CYM(K). Additive primary color space descriptions are generally associated with images displayed on light generating devices, such as monitors or projectors. Subtractive primary color space descriptions are generally associated with images generated on non-light generating devices, such as paper printouts. In order to move an image from a display to a fixed medium, such as paper, a conversion must be made between color spaces associated with electronic encoding of documents.
The concepts disclosed herein are better appreciated with an understanding of various numeric models used to represent images, and image colorization, in image processing or rendering applications. One of the first mathematically defined color spaces was the CIE XYZ color space (also known as CIE 1931 color space), created by CIE in 1931. A human eye has receptors for short (S), middle (M), and long (L) wavelengths, also known as blue, green, and red receptors. One need only generate three parameters to describe a color sensation. A specific method for associating three numbers (or tristimulus values) with each color is called a color space, of which the CIE XYZ color space is one of many such spaces. The CIE XYZ color space is based on direct measurements of the human eye, and serves as the basis from which many other color spaces are defined.
In the CIE XYZ color space, tristimulus values are not the S, M and L stimuli of the human eye, but rather a set of tristimulus values called X, Y, and Z, which are also roughly red, green and blue, respectively. Two light sources may be made up of different mixtures of various colors, and yet have the same color (metamerism). If two light sources have the same apparent color, then they will have the same tristimulus values irrespective of what mixture of light was used to produce them.
CIE L*a*b* (CIELAB or Lab) is frequently thought of as one of the most complete color models. It is used conventionally to describe all the colors visible to the human eye. It was developed for this specific purpose by the International Commission on Illumination (Commission Internationale d'Eclairage, resulting in the acronym CIE). The three parameters (L, a, b) in the model represent the luminance of the color (L: L=0 yields black and L=100 indicates white), its position between red and green (a: negative values indicate green, while positive values indicate red), and its position between yellow and blue (b: negative values indicate blue and positive values indicate yellow).
The Lab color model has been created to serve as a device independent reference model. It is therefore important to realize that visual representations of the full gamut (available range) of colors in this model are not perfectly accurate, but are used to conceptualize a color space. Since the Lab model is three dimensional, it is represented properly in a three dimensional space. A useful feature of the model is that the first parameter is extremely intuitive: changing its value is like changing the brightness setting in a TV set. Therefore only a few representations of some horizontal “slices” in the model are enough to conceptually visualize the whole gamut, wherein the luminance is suitably represented on a vertical axis.
The Lab model is inherently parameterized correctly. Accordingly, no specific color spaces based on this model are required. CIE 1976 L*a*b* or Lab mode is based directly on the CIE 1931 XYZ color space, which sought to define perceptibility of color differences. Circular representations in Lab space correspond to ellipses in XYZ space. Non-linear relations for L*, a*, and b* are related to a cube root, and are intended to mimic the logarithmic response of the eye. Coloring information is referred to the color of the white point of the system.
Electronic documents, such as documents that describe color images, are typically encoded in one or more standard formats. While there are many such formats, representative descriptions currently include Microsoft Word file (*.doc), tagged information file format (“TIFF”), graphic image format (“GIF”), portable document format (“PDF”), Adobe Systems' PostScript, hypertext markup language (“HTML”), extensible markup language (“XML”), drawing exchange files (*.dxf), drawing files (*.dwg), Paintbrush files (*.pcx), Joint Photographic Expert Group (“JPEG”), as well as a myriad of other bitmapped, encoded, compressed or vector file formats.
It would be advantageous to have a system and method that allowed for ready conversion of any such encoded images to address loss of image quality associated with portions of an image being subject to different illumination or lighting characteristics.